Many developed countries, such as Japan and those in the European Union (EU), have been experiencing farmland abandonment problems for decades. The determinants of abandonment are a key concern for government policymakers and researchers [1
]. This is the case because generally, farmland has been cultivated over a long period of time and once it is degraded, it not only incurs huge costs to restore but also has a significant impact on the surrounding ecosystem [8
]. In addition, farmland is an important and essential food production factor and, therefore, the prevention of abandonment is an important component of food security [3
There have been many analyses of the causes of farmland abandonment but relatively little discussion of what kind of data should be used in these analyses. In Japan, where the farmland abandonment problem is becoming increasingly serious, many researchers have analyzed the determinants of farmland abandonment [12
]. They have used data from the census surveys conducted by the Ministry of Agriculture, Forestry and Fisheries (MAFF) every five years as the basis [20
]. However, these data are based on subjective abandoned areas as declared by farmers. Therefore, it may contain measurement errors based on subjective judgments and farmers’ social desirability biases [21
Fortunately, in Japan, apart from these subjective data, field surveys are conducted under the initiative of agricultural committees (called “Nougyo iinkai”) established in each municipality, with some of these surveys compiled into databases. This type of survey, called “farmland patrol,” is regulated by the amended Agricultural Land Act and is conducted at least once a year on all farmland [22
]. However, this information is not available to the public because it contains sensitive personal information. To conduct this analysis, the research institute to which the author belongs signed an academic cooperation agreement with a municipality, Yabu City, Hyogo Prefecture, Japan, to obtain permission to use these databases, which are managed using GIS software. The difference in performance between these (GIS-based) data (GBD) and the conventional (census-based) data (CBD) was examined.
In Japan and in many other countries where agriculture has lost its comparative advantage due to economic development, the increase in abandoned farmland has become a major problem. Japan was one of the first Asian countries to achieve industrialization, and this economic development has increased the opportunity cost of agricultural labor [24
]. However, it was inevitable that Japan would lose its comparative advantage in agriculture due to the inelastic supply of farmland and the small size of farmland per farmer, which would consequently increase the transaction costs of farmland among farmers [25
]. In addition, socioeconomic factors such as a declining and aging population have led to a shortage of agricultural labor, so the abandonment of farmland is accelerating. In other words, extreme labor-saving and capital-intensive agriculture have developed, and rice farming, the most important part of Japanese agriculture, requires a huge initial investment, making it difficult for the younger generation to easily gain entry. In developed countries, farmland abandonment has become a national food security issue in the event of shocks such as crop failure and embargoes [28
]; thus, the Japanese government has positioned its prevention as an important policy issue [30
]. In addition, farmland abandonment is accompanied by a loss of multifunctionality in agriculture [32
An increase in abandoned farmland means a decrease in the essential factor of agricultural production, and the cost of farmland reclamation is enormous. It also has a negative impact on the government’s long-standing efforts to increase food self-sufficiency in Japan [33
]. Given this situation, the Japanese government clearly states in its Basic Plan for Food, Agriculture and Rural Areas (approved by the Cabinet on 31 March 2020) that the prevention of abandoned farmland will be addressed as follows [9
In order to prevent and eliminate abandoned farmland, we will strategically carry out measures such as promoting discussions on the future use of farmland in communities and villages through “payments for activities to enhance multifunctionality” and “direct payments to farmers in hilly and mountainous areas,” supporting collective actions, mitigating damage to crops through bird and animal damage, promoting the consolidation of farmland through “farmland intermediary management projects,” and effectively implementing the development of farmland infrastructure.
As referenced, the prevention of farmland abandonment is closely related to payments for activities to enhance multifunctionality (PAEMF) and direct payments to farmers in hilly and mountainous areas (DPFHM) among the Japanese direct payment schemes. The DPFHM scheme, in particular, is designed to compensate for the difference in production costs between the flatlands and the group of hilly and mountainous areas, and it strongly supports agriculture in the disadvantaged areas in Japan. On the other hand, it imposes a rule that all subsidies received during the support period must be returned when abandoned farmland occurs, which effectively functions as a policy “conditionality” [7
]. Thus, the accuracy of data used for farmland abandonment analysis is important to accurately estimate the causal effects of these policies.
A review paper by Huang et al. [36
] points out that research on farmland abandonment has increased in the last decade. They argue that among the factors of farmland abandonment, socioeconomic factors are the most important and that research on them will continue to be important. Until now, how abandoned farmland affects the natural environment and ecosystems has been important in research fields such as landscape, and similar research has been conducted in Japan [37
]. Terres et al. [29
] examined the drivers of farmland abandonment in the EU using large-scale and extensively aggregated data. However, they acknowledged that farmland abandonment is a local phenomenon and that local data are needed to estimate its risk.
Corbelle-Rico et al. [38
] also conducted a causal analysis of long-term farm abandonment in Spain using categorical data and multinomial logistic models and found that the phenomenon is a complex local phenomenon that needs to be analyzed using at least municipal-level data. Shi et al. [39
] used GIS-processed data and multiple regression analysis to analyze the factors of agricultural land abandonment in mountainous areas in China, but the analysis does not consider a sufficient number of socioeconomic factors and related policies. The community data used in this study were much more localized and allowed the current analysis to be tailored to local conditions (in this study, each community has only about 20 farmers on average).
In Japan, most studies on the farmland abandonment problem use data from the agricultural censuses. One study was conducted in 2011 by Takayama and Nakatani [16
] using a rich dataset, which covered six prefectures in Japan. However, they used census data prior to 2000, with a binary dependent variable indicating whether a community had abandoned farmland. Now that almost every community has a sizeable amount of abandoned farmland, analyses need to focus on the percentage of abandoned farmland area rather than a simple binary variable.
Early studies in 1998 of farmland abandonment in Japan used data on individual farmers from agricultural census data, for example, Senda [12
] and Senda [13
]. Although individual-level data, now unavailable due to restrictions of the Personal Information Protection Law, are attractive, they have the same problem as that in the study by Takayama and Nakatani [16
], as they are used as binary variables of farmland abandonment. In addition, these studies did not consider variables related to regional agricultural structure, so no implications for regional policies were obtained.
In 2018, Su [19
] analyzed the determinants of farmland abandonment using GIS data, and in 2014 Matsui [17
] developed an estimation model for the area of abandoned farmland using machine learning (generalized linear models, random forest, and multivariate adaptive regression splines). However, the data used in these studies were also census-based. The current study used data based on objective measurements by a third party rather than census data based on subjective statements about farming and farmland. The novelty of this study lies in the fact that it used objective data and GIS data-processing (by ArcGIS 10.8) to accurately estimate the abandoned farmland rate model, then the data’s estimation result was compared with the result from conventional data, ultimately deriving a new, more accurate estimation result.
Specifically, the survey-based data measured by a third person were defined as objective data (GBD), while the conventional data reported by farmers were treated as subjective data (CBD). After examining the characteristics of both types of data, their performance was compared using a statistical analysis of the determinants of farmland abandonment. The second objective was to refine the model of these mechanisms by adding new variables, such as altitude and slope maps, bird and animal damage data, and direct subsidy payment data in terms of monetary amounts, which were obtained from Yabu City. These data have not been used in previous similar analyses. The statistical models used to analyze the determinants of abandoned farmland were the ordinary least squares (OLS) method and a modified version of it, the Tobit model.
The structure of this article is as follows. Section 2
provides an overview of the author’s field of study, the estimation model and the data, the most important element of this study. Section 3
presents the estimation results and a comparison of the models. The implications of the results and the limitations of this study are discussed in Section 4
, and the conclusion summarizes the findings of this study.
The results of the estimation (Table 2
) support many of the hypotheses. However, the results show that the significant variables tend to depend on the type of data used. An important finding is that AFR-C
data are compatible with CBD data, and AFR-G
data are compatible with geographic information and other data. The meaning of the word “compatible” here is that the coefficients of a variable are likely to pass the test (be identified). Presumably, census-derived subjective data are highly correlated with census-derived data; namely, there is a possibility of a high correlation between the same types of data. For example, the AFR-C
is likely to be effective when analyzing the relationship between community structure and farmland abandonment from the CBD. However, in the case of objective data, such as geographic information or original data, it is more efficient to use AFR-G
The important point is the relationship between the AFR and the original data, such as DEER
, and PAEMF
. The latter two are particularly important, as these kinds of data have recently been used for evidence-based policy research, which needs to capture causal relationships accurately. The results show that AFR-G
is more accurate in capturing the causal relationships. In particular, previous studies have demonstrated that direct payments for hilly and mountainous areas reduce farmland abandonment [7
]. In this study, the relationship between DPFHM
and AFR was significant only in the AFR-G
model. Hence, in the field of policy research, the use of objective data such as AFR-G
and GIS is likely to be more effective.
The results also show that ECD can generate bias. In Japan, MAFF avoids publishing data on a community with two or fewer households to protect personal information. While such treatment is inevitable from the perspective of privacy protection, it may distort policy evaluation and judgments in policy decision-making. Therefore, it is necessary to establish a system in which comprehensive data (without selection bias), such as the ACD, can be used for policy purposes and academic research.
The implications of the estimation results of the AFR-G validated in this study were reviewed. Geographical conditions that are unfavorable to agriculture, such as high altitude and steep slope, naturally induce farmland abandonment. The occurrence of bird and animal damage is also likely to reduce farmers’ motivation to cultivate. Therefore, strong measures to prevent birds and animals from entering the community are also required. The most important policy implication is that the direct payment for hilly and mountainous areas is effective against farmland abandonment. This is a finding that is not possible when using the binary AFR-C data.
Let us now discuss the position of the problem of abandoned farmland from international and policy perspectives: the international literature on abandoned farmland in the EU is vast, but most of it is concerned with the impact of abandoned farmland, and little of it identifies the drivers of abandonment. Some studies suggest that public policy exogenously influences farmland abandonment but that socioeconomic factors and ownership systems dominate its dynamics [57
In the EU, many researchers have proposed amendments to the Common Agricultural Policy (CAP) in response to the increase in abandoned farmland [58
]. However, there are two main opinions on the impact of abandoned farmland: one is that abandonment harms the environment, and the other is that abandonment improves the environment by returning farmland to the wilderness. Dolton-Thornton [58
] argues that the CAP’s agro-environmental measures are still too productive and neoliberal to address the problem of abandoned farmland as a part of them and that they need to be repositioned to address population decline, including in the rural non-farm sector.
Since the population decline in rural areas is also serious in Japan, it is believed that the problem of abandoned farmland needs to be considered as a part of regional economic policy, beyond the scope of mere agricultural policy. As Terres et al. [29
] argue in the EU case, food security is an extremely important factor in Japan, where the grain self-sufficiency rate has fallen to 30%, and securing the potential of agricultural production is a policy that is not only materially but also psychologically important for the Japanese people.
Finally, this study has some limitations. It used 2010 data from Yabu City in Hyogo Prefecture, one of the many municipalities in Japan; therefore, the limitation of the external validity of the results should be noted. However, because 73% of the country’s land and 41% of its farmland belong to hilly and mountainous areas such as the area of this study [59
], and Japanese society and communities are relatively homogeneous in character [60
], the results of this study should have some generalizability in Japan. Moreover, since the academic value of this study is in the estimation and use of data, the results are sufficient to point out the problems in a particular case (i.e., in at least one case, a problem arises when using conventional materials and methods). Although this study addresses the case of Japan, where rice farming dominates, it is only a matter of time before similar problems emerge in the rest of East Asia, which is experiencing rapid economic development. In fact, problems of farmland abandonment are beginning to arise in China according to a 2020 study by Zhou [61
], and the analysis of Japan will provide useful insights for these regions.
Another shortcoming concerns the estimation method. When presenting the estimation results, the causal relationship between some variables and the AFR was mentioned. However, causal effects, such as policy and socially operative variables, require close attention to their identification. For example, the results show that damage from birds and animals tends to increase the amount of abandoned land, but the opposite causal relationship can also be assumed. Specifically, abandoned land tends to attract birds and animals to the community. Due to data limitations, it was not possible to examine these endogenous issues. These issues need to be examined based on more extensive data collection and advanced estimation methods, such as the instrumental variable (IV) method (if IV is available) [62
This study mainly focused on the data problem related to analyzing the determinants of the farmland abandonment problem, which is becoming a serious problem in developed countries such as Japan, where agriculture has lost its comparative advantage. The main findings are as follows.
The estimation results differed if objective or subjective data were used.
There is a possibility of bias in the estimation when conventional census data, which farmers subjectively provide, was used in the analysis.
Correlations (coefficient parameters) between the same types of data (objective or subjective) are easy to identify.
When using data restricted from the perspective of privacy protection, a bias occurred in some estimates, but it did not reach a serious level. (This is the issue of confidentiality of community-based data, which has been used in many studies on Japanese agricultural policy.)
The important implication of the third finding mentioned above is that while the variables from CBD data are more compatible with each other, variables from GIS are more compatible with the same type of data (GIS-related data). In addition, other original data, such as policy variables, were shown to be more compatible with objective data such as GIS data.
In recent years, there has been a rapid development of analytical methods in microeconomic analysis and policy research, but it is assumed that the data used in these analyses are accurately measured. However, as seen in this study, when there are differences in measures of land use conditions, or when the data are not fully available for social reasons, there may be differences or biases in the results. Therefore, when using land use data, the origin of the data should be carefully considered before conducting an analysis.